Autofluorescence in liver tissue is heterogeneous and dynamically altered during metabolic liver disease, complicating spectral flow cytometry analysis. We provide a structured framework for identifying and handling condition-dependent autofluorescence, demonstrating that inadequate autofluorescence correction biases macrophage quantification and subset resolution in complex liver samples.
ABSTRACT
Autofluorescence (AF) in biological tissues arises from the natural emission of light by intra and extracellular molecules upon light absorption. Conventional flow cytometry cannot correct for cellular AF, leading to distorted signals and measurement errors. Although spectral flow cytometry enables AF visualization and extraction, accurately correcting for AF remains challenging in complex biological samples containing multiple cell types with distinct AF properties, such as the liver. Additionally, pathological processes such as inflammation and fibrosis can alter tissue composition and activate specific cell types, further modifying AF characteristics across experimental conditions. Macrophages are among the most AF immune cells, exhibiting fluorescence emission across the entire spectrum of light. Recent studies have demonstrated substantial heterogeneity in the phenotypes of resident and recruited macrophages both in the healthy liver and during metabolic dysfunction-associated steatohepatitis (MASH). Given their critical role in liver disease pathophysiology, we applied a spectral flow cytometry strategy to better analyze macrophage subpopulations in healthy and MASH murine livers. Our findings show that healthy, steatotic, and MASH livers exhibit distinct and heterogeneous AF signatures. Furthermore, inadequate AF extraction compromised accurate quantification of hepatic macrophages and differentiation of macrophage subsets.